Decision Matrix Builder for Any Major Choice
Why this prompt matters
Most people make complex decisions on gut feel, which introduces cognitive biases like anchoring (over-weighting the first option seen) and recency bias (favoring the last option considered). A weighted scoring matrix forces you to define what matters before you evaluate options, separating criteria-setting from scoring — the same technique used in formal procurement, engineering design reviews, and McKinsey-style strategy work. The result is a defensible, documented decision you can explain to a team.
What we use it for
Use this prompt when you are facing a significant decision with multiple options and competing priorities — choosing a new tech stack, picking a vendor, deciding between job offers, evaluating business strategies, or selecting among product features to build next quarter.
Prompt
Act as a senior strategy consultant who specializes in structured decision-making frameworks. I need to make a major decision and want to use a weighted scoring matrix to evaluate my options objectively. My decision: [DESCRIBE YOUR DECISION IN 1-2 SENTENCES] My options: 1. [OPTION A] 2. [OPTION B] 3. [OPTION C] (Add more if needed) My criteria for evaluating these options (list what matters most): - [CRITERION 1, e.g. cost] - [CRITERION 2, e.g. implementation time] - [CRITERION 3, e.g. team capability] - [CRITERION 4, e.g. long-term scalability] (Add or remove criteria as needed) Constraints and context: - [ANY HARD CONSTRAINTS — e.g. budget cap, deadline, must-have features] - [RELEVANT CONTEXT — e.g. team size, current situation, strategic goals] Do the following: 1. Review my criteria and suggest adjustments if any important factor is missing or if two criteria overlap significantly. 2. Ask me to assign weights to each criterion (1-5, where 5 = most important) — or assign sensible weights yourself based on the context I provided and explain your reasoning. 3. Score each option against every criterion on a scale of 1-10. 4. Calculate the weighted score for each option (score × weight, summed). 5. Present a clean scoring table with: criteria | weight | scores per option | weighted scores | totals. 6. Identify the winning option and explain in 2-3 sentences why it scored highest. 7. Flag any minority concern: if the runner-up is close or if the top scorer has a critical weakness, call it out explicitly. Output format: - Start with the scoring table (use markdown table formatting) - Follow with a 3-paragraph analysis: winner rationale, key tradeoffs, one risk to watch
Result
<h3>Scoring Matrix: Cloud Provider for Our SaaS Product</h3><table><thead><tr><th>Criterion</th><th>Weight</th><th>AWS</th><th>GCP</th><th>Azure</th><th>AWS x W</th><th>GCP x W</th><th>Azure x W</th></tr></thead><tbody><tr><td>Cost at scale</td><td>5</td><td>7</td><td>8</td><td>6</td><td>35</td><td>40</td><td>30</td></tr><tr><td>ML/AI tooling</td><td>4</td><td>8</td><td>9</td><td>7</td><td>32</td><td>36</td><td>28</td></tr><tr><td>Team familiarity</td><td>5</td><td>9</td><td>5</td><td>6</td><td>45</td><td>25</td><td>30</td></tr><tr><td>Enterprise support</td><td>3</td><td>9</td><td>7</td><td>9</td><td>27</td><td>21</td><td>27</td></tr><tr><td>Vendor lock-in risk</td><td>3</td><td>5</td><td>6</td><td>5</td><td>15</td><td>18</td><td>15</td></tr><tr><td><strong>TOTAL</strong></td><td><strong>20</strong></td><td></td><td></td><td></td><td><strong>154</strong></td><td><strong>140</strong></td><td><strong>130</strong></td></tr></tbody></table><p><strong>Winner: AWS (154 points)</strong></p><p>AWS wins primarily because of team familiarity (weight 5), which carries the most points. Ramp-up time for a new platform would cost 3-4 engineering months — a real cost that GCP marginal ML advantage does not offset at your current AI workload scale.</p><p><strong>Key tradeoff:</strong> GCP leads on ML/AI tooling and is the better long-term bet if AI inference becomes your core product. If your roadmap shifts heavily toward custom model serving in the next 12 months, revisit this matrix with ML tooling weighted at 5 instead of 4 — GCP would likely overtake AWS.</p><p><strong>Risk to watch:</strong> AWS scored lowest on vendor lock-in risk across all three providers. Before committing, audit which AWS-specific services you plan to use. If the answer includes proprietary services like Lambda, DynamoDB, and SageMaker together, your vendor dependency compounds fast.</p>
<h2>Stop Making Important Decisions on Gut Feel</h2><p>Every significant decision has the same problem: too many options, too many criteria, and a brain that is wired to take shortcuts. You weigh options in your head, get swayed by whichever one you considered most recently, and end up with a choice you cannot clearly defend.</p><p>The weighted decision matrix is one of the oldest and most reliable tools in structured decision-making — used in government procurement, engineering design reviews, and management consulting for decades. This prompt brings that framework directly into Claude.</p><h2>What the Prompt Does</h2><p>You describe your decision, list your options, and specify the criteria that matter to you. The AI first audits your criteria for gaps or overlaps, then assigns weights, scores each option, computes the matrix, and delivers a scored table plus a three-paragraph analysis: winner rationale, key tradeoffs, and one risk to watch.</p><p>The constraint-setting step is deliberate. By defining weights <em>before</em> seeing the scores, you prevent yourself from reverse-engineering the criteria to favor a predetermined answer — a common failure mode in informal decision processes.</p><h2>When to Use It</h2><ul><li>Choosing a technology platform or vendor</li><li>Evaluating job offers or career moves</li><li>Selecting among product features for the next sprint or quarter</li><li>Comparing business strategies or go-to-market approaches</li><li>Any decision where you need a documented rationale for a team or stakeholder</li></ul><h2>Works Best With</h2><p>Claude Sonnet 4.6 handles the structured output and multi-step reasoning well. GPT-4o is a solid alternative. For decisions with more than five criteria or six options, consider breaking the analysis into two passes: criteria weighting first, then scoring.</p>